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Crowdsourcing annotation system of object counting dataset for deep learning algorithm

IOP Conference Series: Earth and Environmental Science 2018年第1期195卷

作者： Tjeng Wawan Cenggoro Fidelson Tanzil Ayu Hidayah Aslamiah Ettikan Kandasamy Karuppiah Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 NVIDIA Corporation Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

Deep Learning is currently the state-of-the-art technique for various Computer Vision tasks, including object counting. Despite of its high performance, Deep Learning requires a gigantic amount of training data to show its best result. Getting this massive data in reasonable time requires a proper strategy such as crowdsourcing. However, in case of object counting, we found no crowdsourcing system able to effectively collect necessary data. To tackle this problem, we develop a crowdsourcing system to annotate image for object counting dataset. This system is also equipped with validation system to ensure the quality of collected dataset.

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Publisher Correction: A phenotypic spectrum of autism is attributable to the combined effects of rare variants, polygenic risk and sex

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Nature genetics 2022年第8期54卷 1259页

作者： Danny Antaki James Guevara Adam X Maihofer Marieke Klein Madhusudan Gujral Jakob Grove Caitlin E Carey Oanh Hong Maria J Arranz Amaia Hervas Christina Corsello Keith K Vaux Alysson R Muotri Lilia M Iakoucheva Eric Courchesne Karen Pierce Joseph G Gleeson Elise B Robinson Caroline M Nievergelt Jonathan Sebat Biomedical Sciences Graduate Program University of California San Diego La Jolla CA USA. Beyster Center for Psychiatric Genomics University of California San Diego La Jolla CA USA. Department of Psychiatry University of California San Diego La Jolla CA USA. Department of Cellular and Molecular Medicine University of California San Diego La Jolla CA USA. Institute for Genomic Medicine University of California San Diego La Jolla CA USA. Department of Neurosciences University of California San Diego La Jolla CA USA. Department of Biomedicine Aarhus University Aarhus Denmark. The Lundbeck Foundation Initiative for Integrative Psychiatric Research iPSYCH Aarhus Denmark. Center for Genomics and Personalized Medicine and Center for Integrative Sequencing iSEQ Aarhus Denmark. Bioinformatics Research Centre Aarhus University Aarhus Denmark. Harvard T.H. Chan School of Public Health Broad Institute of the Massachusetts Institute of Technology and Harvard Cambridge MA USA. Research Laboratory Unit Fundacio Docencia i Recerca Mutua Terrassa Spain. Child and Adolescent Mental Health Unit Hospital Universitari Mútua de Terrassa Barcelona Spain. TEACCH Autism Program University of North Carolina Chapel Hill NC USA. Human Longevity Inc. San Diego CA USA. Department of Pediatrics and Department of Cellular & Molecular Medicine University of California San Diego School of Medicine Center for Academic Research and Training in Anthropogeny Archealization Center Kavli Institute for Brain and Mind La Jolla CA USA. Autism Center of Excellence University of California San Diego La Jolla CA USA. Beyster Center for Psychiatric Genomics University of California San Diego La Jolla CA USA. jsebat@ucsd.edu. Department of Psychiatry University of California San Diego La Jolla CA USA. jsebat@ucsd.edu. Department of Cellular and Molecular Medicine University of California San Diego La Jolla CA USA. jsebat@ucsd.edu. Institute for Genomic Medicine University of Californi

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Doubly Penalized LASSO for Reconstruction of Biological Networks

Doubly Penalized LASSO for Reconstruction of Biological Netw...

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作者： Asadi, Behrang Maurya, Mano Ram Tartakovsky, Daniel M. Subramaniam, Shankar Departments of Bioengineering and Mechanical and Aerospace Engineering University of California San Diego Danaher Labs la Jolla Santa ClaraCA United States San Diego Supercomputer Center Department of Bioengineering University of California San Diego San DiegoCA United States Department of Mechanical Aerospace Engineering University of California San Diego San DiegoCA United States Department of Bioengineering Departments of Computer Science and Engineering Cellular and Molecular Medicine and the Graduate Program in Bioinformatics University of California San Diego San DiegoCA United States

Reconstruction of biological and biochemical networks is a crucial step in extracting knowledge and causal information from large biological data sets. This task is particularly challenging when dealing with time-series data from dynamic networks. We have developed a new method, called doubly penalized least absolute shrinkage and selection operator (DPLASSO), for the reconstruction of dynamic biological networks. Our method consists of two components: statistical significance testing of model coefficients and penalized/constrained optimization. A partial least squares (PLS) with statistical significance testing acts as a supervisory-level filter to extract the most informative components of the network from a data set. Then, LASSO with extra weights on the smaller parameters identified in the first layer is employed to retain the main predictors and to set the smallest coefficients to zero. We present two case studies to compare the relative performance of DPLASSO and LASSO in terms of several metrics, such as sensitivity, specificity, and accuracy. The first case study employs a synthetic data set;it demonstrates that DPLASSO substantially improves network reconstruction in terms of accuracy and specificity. The second case study relies on simulated data sets for cell division cycle of fission yeast and shows that DPLASSO overall outperforms LASSO and PLS in terms of sensitivity, specificity, and accuracy. Combining PLS with statistical significance testing and LASSO provides good performance with respect to several metrics. DPLASSO is well suited for reconstruction of networks with low and moderate density. For high-density networks, high specificity is desired, making PLS a more suitable approach. © 1963-2012 IEEE.

关键词： Least squares approximations

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Author Correction: Wiskott-Aldrich syndrome protein forms nuclear condensates and regulates alternative splicing

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Nature communications 2022年第1期13卷 5291页

作者： Baolei Yuan Xuan Zhou Keiichiro Suzuki Gerardo Ramos-Mandujano Mengge Wang Muhammad Tehseen Lorena V Cortés-Medina James J Moresco Sarah Dunn Reyna Hernandez-Benitez Tomoaki Hishida Na Young Kim Manal M Andijani Chongwei Bi Manching Ku Yuta Takahashi Jinna Xu Jinsong Qiu Ling Huang Christopher Benner Emi Aizawa Jing Qu Guang-Hui Liu Zhongwei Li Fei Yi Yanal Ghosheh Changwei Shao Maxim Shokhirev Patrizia Comoli Francesco Frassoni John R Yates Xiang-Dong Fu Concepcion Rodriguez Esteban Samir Hamdan Juan Carlos Izpisua Belmonte Mo Li Bioscience Program Biological and Environmental Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Kingdom of Saudi Arabia. Gene Expression Laboratory Salk Institute for Biological Studies 10010 North Torrey Pines Road La Jolla CA 92037 USA. Institute for Advanced Co-Creation Studies Graduate School of Engineering Science Osaka University Osaka Japan. Department of Cell Biology Scripps Research Institute La Jolla CA 92037 USA. The Waitt Advanced Biophotonics Core Facility Salk Institute for Biological Studies 10010 North Torrey Pines Road La Jolla CA 92037 USA. Altos Labs Inc. 5510 Morehouse Drive Suite 300 San Diego CA 92121 USA. Laboratory of Biological Chemistry School of Pharmaceutical Sciences Wakayama Medical University 25-1 Shitibancho Wakayama Wakayama 640-8156 Japan. Next-generation sequencing core Salk Institute for Biological Studies 10010 North Torrey Pines Road La Jolla CA 92037 USA. Life Science Center Tsukuba Advanced Research Alliance University of Tsukuba 1-1-1 Tennoudai Tsukuba Ibaraki 305-8577 Japan. Department of Cellular & Molecular Medicine University of California at San Diego La Jolla CA 92093 USA. Integrative Genomics and Bioinformatics Core Salk Institute for Biological Studies 10010 North Torrey Pines Road La Jolla CA 92037 USA. State Key Laboratory of Membrane Biology Institute of Zoology Chinese Academy of Sciences Beijing 100101 China. University of Southern California 1333 San Pablo Street MMR 618 Los Angeles CA 90033 USA. Ambys Medicines 131 Oyster Point Blvd. Suite 200 South San Francisco CA 94080 USA. Pediatric Hematology/Oncology and Cell Factory Fondazione IRCCS Policlinico San Matteo Pavia Italy. Department of Research Laboratories and Director of Center for Stem Cell and Cell Therapy Instituto G. Gaslini Children Hospital Scientific Institute 16147 Genova Italy. Bioscience Program Biological and Environmental Science and Engineering

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Functional annotation of chemical libraries across diverse biological processes (vol 13, pg 982, 2017)

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NATURE CHEMICAL BIOLOGY 2017年第12期13卷 1286-1286页

作者： Piotrowski, Jeff S. Li, Sheena C. Deshpande, Raamesh Simpkins, Scott W. Nelson, Justin Yashiroda, Yoko Barber, Jacqueline M. Safizadeh, Hamid Wilson, Erin Okada, Hiroki Gebre, Abraham A. Kubo, Karen Torres, Nikko P. LeBlanc, Marissa A. Andrusiak, Kerry Okamoto, Reika Yoshimura, Mami DeRango-Adem, Eva van Leeuwen, Jolanda Shirahige, Katsuhiko Baryshnikova, Anastasia Brown, Grant W. Hirano, Hiroyuki Costanzo, Michael Andrews, Brenda Ohya, Yoshikazu Osada, Hiroyuki Yoshida, Minoru Myers, Chad L. Boone, Charles RIKEN Center for Sustainable Resource Science Wako Saitama Japan Department of Computer Science and Engineering University of Minnesota-Twin Cities Minneapolis Minnesota USA Bioinformatics and Computational Biology Program University of Minnesota-Twin Cities Minneapolis Minnesota USA Department of Electrical and Computer Engineering University of Minnesota-Twin Cities Minneapolis Minnesota USA Department of Integrated Biosciences Graduate School of Frontier Sciences University of Tokyo Kashiwa Chiba Japan Donnelly Centre University of Toronto Toronto Ontario Canada Institute of Molecular and Cellular Biosciences Center for Epigenetic Disease University of Tokyo Bunkyo-ku Tokyo Japan Lewis-Sigler Institute for Integrative Genomics Princeton University Princeton New Jersey USA Department of Biochemistry University of Toronto Toronto Ontario Canada.

Chemical-genetic approaches offer the potential for unbiased functional annotation of chemical libraries. Mutations can alter the response of cells in the presence of a compound, revealing chemical-genetic interactions that can elucidate a compound’s mode of action. We developed a highly parallel, unbiased yeast chemical-genetic screening system involving three key components. First, in a drug-sensitive genetic background, we constructed an optimized diagnostic mutant collection that is predictive for all major yeast biological processes. Second, we implemented a multiplexed (768-plex) barcode-sequencing protocol, enabling the assembly of thousands of chemical-genetic profiles. Finally, based on comparison of the chemical-genetic profiles with a compendium of genome-wide genetic interaction profiles, we predicted compound functionality. Applying this high-throughput approach, we screened seven different compound libraries and annotated their functional diversity. We further validated biological process predictions, prioritized a diverse set of compounds, and identified compounds that appear to have dual modes of action.

关键词： Chemical genetics High-throughput screening Networks and systems biology Target identification

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Severe Congenital Neutropenia-Associated Mutations Reveal Stage-Specific Gfi1-Dependent Checkpoints in Myeloid Development

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BLOOD 2017年 130卷 544-544页

作者： Muench, David Kappes, Dietmar J. Salomonis, Nathan Myers, Kasiani C. Grimes, Lee Molecular and Developmental Biology Graduate Program Cincinnati Children's Hospital Medical Center Cincinnati OH Fox Chase Cancer Center Blood Cell Development and Function Program Philadelphia PA Bioinformatics Cincinnati Children's Hospital Medical Center Cincinnati OH Cincinnati Children's Hospital Cincinnati OH Division of Immunobiology Cincinnati Children's Hospital Medical Center Cincinnati OH

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AKT3-mediated IWS1 phosphorylation promotes the proliferation of EGFR-mutant lung adenocarcinomas through cell cycle-regulated U2AF2 RNA splicing

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Nature communications 2021年第1期12卷 4624页

作者： Georgios I Laliotis Evangelia Chavdoula Maria D Paraskevopoulou Abdul Kaba Alessandro La Ferlita Satishkumar Singh Vollter Anastas Keith A Nair Arturo Orlacchio Vasiliki Taraslia Ioannis Vlachos Marina Capece Artemis Hatzigeorgiou Dario Palmieri Christos Tsatsanis Salvatore Alaimo Lalit Sehgal David P Carbone Vincenzo Coppola Philip N Tsichlis Department of Cancer Biology and Genetics The Ohio State University Columbus OH USA. glaliot1@jhmi.edu. The Ohio State University Comprehensive Cancer Center-Arthur G. James Cancer Hospital and Richard J. Solove Research Institute Columbus OH USA. glaliot1@jhmi.edu. School of Medicine University of Crete Heraklion Crete Greece. glaliot1@jhmi.edu. Department of Oncology Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA. glaliot1@jhmi.edu. Department of Cancer Biology and Genetics The Ohio State University Columbus OH USA. The Ohio State University Comprehensive Cancer Center-Arthur G. James Cancer Hospital and Richard J. Solove Research Institute Columbus OH USA. Molecular Oncology Research Institute Tufts Medical Center Boston MA USA. Department of Clinical and Experimental Medicine Bioinformatics Unit University of Catania Catania Italy. Department of Medicine Division of Hematology The Ohio State University Columbus OH USA. Tufts Graduate School of Biomedical Sciences Program in Genetics Boston MA USA. Center of Basic Research Biomedical Research Foundation of the Academy of Athens Athens Greece. DIANA-Lab Hellenic Pasteur Institute Athens Greece. Department Of Pathology Beth Israel-Deaconess Medical Center Harvard Medical School Boston MA USA. School of Medicine University of Crete Heraklion Crete Greece. Institute of Molecular Biology and Biotechnology Foundation for Research and Technology Hellas Heraklion Crete Greece. Department of Internal Medicine Division of Medical Oncology The Ohio State University Medical Center Columbus OH USA. Department of Cancer Biology and Genetics The Ohio State University Columbus OH USA. Philip.tsichlis@osumc.edu. The Ohio State University Comprehensive Cancer Center-Arthur G. James Cancer Hospital and Richard J. Solove Research Institute Columbus OH USA. Philip.tsichlis@osumc.edu. Tufts Graduate School of Biomedical Sciences Program in Genetics Boston MA USA.

AKT-phosphorylated IWS1 regulates alternative RNA splicing via a pathway that is active in lung cancer. RNA-seq studies in lung adenocarcinoma cells lacking phosphorylated IWS1, identified a exon 2-deficient U2AF2 splice variant. Here, we show that exon 2 inclusion in the U2AF2 mRNA is a cell cycle-dependent process that is regulated by LEDGF/SRSF1 splicing complexes, whose assembly is controlled by the IWS1 phosphorylation-dependent deposition of histone H3K36me3 marks in the body of target genes. The exon 2-deficient U2AF2 mRNA encodes a Serine-Arginine-Rich (RS) domain-deficient U2AF65, which is defective in CDCA5 pre-mRNA processing. This results in downregulation of the CDCA5-encoded protein Sororin, a phosphorylation target and regulator of ERK, G2/M arrest and impaired cell proliferation and tumor growth. Analysis of human lung adenocarcinomas, confirmed activation of the pathway in EGFR-mutant tumors and showed that pathway activity correlates with tumor stage, histologic grade, metastasis, relapse after treatment, and poor prognosis.

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Discovery Biological Motifs Using Heuristics Approaches

Discovery Biological Motifs Using Heuristics Approaches

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Brazilian Conference on Intelligent Systems (BRACIS)

作者： Jader M. Caldonazzo Garbelini André Yoshiaki Kashiwabara Danilo Sipoli Sanches Graduate Program in Bioinformatics (PPGBIOINFO) Federal University of Technology Cornélio Procópio PR Brazil

ISBN: (纸本)9781509035670

The identification of transcription factors binding sites (TFBS) - also called motifs - in DNA sequences is the first step to understanding how works gene regulation. Recognizing these patterns in the promoter regions of co-expressed genes is a determining key for this. Although there are several algorithms for this purpose, the problem is still far from being solved because of the great diversity of gene expression and the binding sites low specificity. State of the art algorithms have limitations, such as the high number of false positives and low accuracy for Identifying weak motifs. In this article we proposed a new approach based on memetic algorithms (DMMA) for discovery mofifs. The proposed approach was developed using evolutionary computation along with the local search algorithms simulated annealing and variable neighborhood search. To attest the algorithm ability, tests were conducted in four datasets - two real and two synthetic - and the results were compared with other approaches in the literature.

关键词： Sociology Statistics Complexity theory Biology Probability Mathematical model Simulated annealing

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Genome-wide association study of long COVID

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Nature genetics 2025年 2025 May 21页

作者： Vilma Lammi Tomoko Nakanishi Samuel E Jones Shea J Andrews Juha Karjalainen Beatriz Cortés Heath E O'Brien Ana Ochoa-Guzman Brian E Fulton-Howard Martin Broberg Hele H Haapaniemi Masahiro Kanai Matti Pirinen Axel Schmidt Ruth E Mitchell Abdou Mousas Massimo Mangino Alicia Huerta-Chagoya Nasa Sinnott-Armstrong Elizabeth T Cirulli Marc Vaudel Alex S F Kwong Amit K Maiti Minttu M Marttila Daniel C Posner Alexis A Rodriguez Chiara Batini Francesca Minnai Anna R Dearman C A Robert Warmerdam Celia B Sequeros Thomas W Winkler Daniel M Jordan Raimonds Rešcenko Lorenzo Miano Jacqueline M Lane Ryan K Chung Beatriz Guillen-Guio Olivia C Leavy Laura Carvajal-Silva Kevin Aguilar-Valdés Erika Frangione Lindsay Guare Ekaterina Vergasova Eirini Marouli Pasquale Striano Ummu Afeera Zainulabid Ashutosh Kumar Hajar Fauzan Ahmad Ryuya Edahiro Shuhei Azekawa Shiuh-Wen Luoh Christian Erikstrup Ole B V Pedersen Jordan Lerner-Ellis Alicia Colombo Joseph J Grzymski Makoto Ishii Yukinori Okada Noam D Beckmann Meena Kumari Ralf Wagner Iris M Heid Catherine John Patrick J Short Per Magnus Laura Ansone Luca V C Valenti Sulggi A Lee Louise V Wain Ricardo A Verdugo Karina Banasik Frank Geller Lude H Franke Alexander Rakitko Emma L Duncan Alessandra Renieri Konstantinos K Tsilidis Rafael de Cid Ahmadreza Niavarani Erik Abner Teresa Tusié-Luna Shefali S Verma George Davey Smith Nicholas J Timpson Ravi K Madduri Kelly Cho Mark J Daly Andrea Ganna Eva C Schulte J Brent Richards Kerstin U Ludwig Michael Marks-Hultström Hugo Zeberg Hanna M Ollila Institute for Molecular Medicine Finland (FIMM) Helsinki Institute of Life Science (HiLIFE) University of Helsinki Helsinki Finland. Department of Human Genetics McGill University Montreal Quebec Canada. Centre for Clinical Epidemiology Department of Medicine Lady Davis Institute Jewish General Hospital McGill University Montreal Quebec Canada. Kyoto-McGill International Collaborative Program in Genomic Medicine Graduate School of Medicine Kyoto University Kyoto Japan. Department of Genome Informatics Graduate School of Medicine the University of Tokyo Tokyo Japan. Research Fellow Japan Society for the Promotion of Science Tokyo Japan. Department of Psychiatry and Behavioral Sciences University of California San Francisco San Francisco CA USA. Program in Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge MA USA. Stanley Center for Psychiatric Research Broad Institute of Harvard and MIT Cambridge MA USA. Analytic and Translational Genetics Unit Massachusetts General Hospital Boston MA USA. Genomes for Life-GCAT Lab CORE Program Germans Trias i Pujol Research Institute (IGTP) Badalona Spain. Grup de REcerca en Impacte de les Malalties Cròniques i les seves Trajectòries (GRIMTra) Barcelona Spain. Sano Genetics Limited London UK. Unidad de Biología Molecular y Medicina Genómica Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán Mexico City Mexico. Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New York City NY USA. Broad Institute Cambridge MA USA. Analytical and Translational Genetics Unit Massachusetts General Hospital Harvard Medical School Boston MA USA. Department of Mathematics and Statistics University of Helsinki Helsinki Finland. Department of Public Health University of Helsinki Helsinki Finland. Institute of Human Genetics University of Bonn School of Medicine and University Hospital Bonn Bonn Germany. MRC Integrative Epidemiology Unit University of Bristol Bristol

Infections can lead to persistent symptoms and diseases such as shingles after varicella zoster or rheumatic fever after streptococcal infections. Similarly, severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) infection can result in long coronavirus disease (COVID), typically manifesting as fatigue, pulmonary symptoms and cognitive dysfunction. The biological mechanisms behind long COVID remain unclear. We performed a genome-wide association study for long COVID including up to 6,450 long COVID cases and 1,093,995 population controls from 24 studies across 16 countries. We discovered an association of FOXP4 with long COVID, independent of its previously identified association with severe COVID-19. The signal was replicated in 9,500 long COVID cases and 798,835 population controls. Given the transcription factor FOXP4's role in lung physiology and pathology, our findings highlight the importance of lung function in the pathophysiology of long COVID.

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Highly accurate model for prediction of lung nodule malignancy with ct scans

arXiv

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arXiv 2018年

作者： Causey, Jason Zhang, Junyu Ma, Shiqian Jiang, Bo Qualls, Jake Politte, David G. Prior, Fred Zhang, Shuzhong Huang, Xiuzhen Department of Computer Science Arkansas State University JonesboroAR72467 United States Department of Industrial and Systems Engineering University of Minnesota MinneapolisMN55455 United States Department of Systems Engineering and Engineering Management Chinese University of Hong Kong Shatin N.T. Hong Kong Research Center for Management Science and Data Analytics School of Information Management and Engineering Shanghai University of Finance and Economics Shanghai200433 China Mallinckrodt Institute of Radiology Washington University St. LouisMO63110 United States Department of Biomedical Informatics University of Arkansas for Medical Sciences Little RockAR72205 United States UALR/UAMS Joint Graduate Program in Bioinformatics Little RockAR72204 United States

Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational approaches to achieve performance comparable to experienced radiologists. Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN). For training and validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort. All nodules were identified and classified by four experienced thoracic radiologists who participated in the LIDC project. NoduleX achieves high accuracy for nodule malignancy classification, with an AUC of ~0.99. This is commensurate with the analysis of the dataset by experienced radiologists. Our approach, NoduleX, provides an effective framework for highly accurate nodule malignancy prediction with the model trained on a large patient population. Our results are replicable with software available at http://***/NoduleX. Copyright © 2018, The Authors. All rights reserved.

关键词： Forecasting

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